#import all required libraires
import pandas as pd
import numpy as np
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', 100)
pd.set_option('display.width',1000)
import matplotlib.cm as cm            # Matplotlib and associated plotting modules
import matplotlib.colors as colors
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans  # import k-means from clustering stage
from sklearn.metrics import silhouette_score
import folium                        # map rendering library
import json # library to handle JSON files
from pandas.io.json import json_normalize
from geopy.geocoders import Nominatim
import requests # library to handle requests
import webbrowser
import pgeocode

#getting cordinates for Mumbai
address = 'Mumbai'
geolocator = Nominatim(user_agent="ny_explorer")
location = geolocator.geocode(address)
latitude = location.latitude
longitude = location.longitude
print('The geograpical coordinate of Mumbai are {}, {}.'.format(latitude, longitude))

The geograpical coordinate of Mumbai are 19.0759899, 72.8773928.

#read data from the webpage
url = 'https://en.wikipedia.org/wiki/List_of_neighbourhoods_in_Mumbai'
df1 = pd.read_html(url)
type(df1)

list

df1

[                                               0                                1          2          3
 0                                           Area                         Location   Latitude  Longitude
 1                                         Amboli          Andheri,Western Suburbs    19.1293    72.8434
 2                               Chakala, Andheri                  Western Suburbs  19.111388  72.860833
 3                                     D.N. Nagar          Andheri,Western Suburbs  19.124085  72.831373
 4                                 Four Bungalows          Andheri,Western Suburbs  19.124714   72.82721
 5                                    Lokhandwala          Andheri,Western Suburbs  19.130815   72.82927
 6                                          Marol          Andheri,Western Suburbs  19.119219  72.882743
 7                                          Sahar          Andheri,Western Suburbs  19.098889  72.867222
 8                                Seven Bungalows          Andheri,Western Suburbs  19.129052  72.817018
 9                                        Versova          Andheri,Western Suburbs      19.12      72.82
 10                                     Mira Road   Mira-Bhayandar,Western Suburbs  19.284167  72.871111
 11                                     Bhayandar   Mira-Bhayandar,Western Suburbs      19.29      72.85
 12                                         Uttan   Mira-Bhayandar,Western Suburbs      19.28     72.785
 13                           Bandstand Promenade           Bandra,Western Suburbs  19.042718  72.819132
 14                                      Kherwadi           Bandra,Western Suburbs    19.0553    72.8314
 15                                     Pali Hill           Bandra,Western Suburbs     19.068     72.826
 16                                   I.C. Colony  Borivali (West),Western Suburbs  19.247039   72.84983
 17                                         Gorai  Borivali (West),Western Suburbs  19.250057  72.782021
 18                                        Dahisa                  Western Suburbs  19.250069  72.859347
 19                             Aarey Milk Colony         Goregaon,Western Suburbs  19.148493  72.881756
 20                                  Bangur Nagar         Goregaon,Western Suburbs  19.167362  72.832252
 21                               Jogeshwari West                  Western Suburbs      19.12      72.85
 22                                          Juhu                  Western Suburbs       19.1      72.83
 23                                       Charkop   Kandivali West,Western Suburbs  19.216182  72.830575
 24                                        Poisar   Kandivali West,Western Suburbs  19.204511  72.837639
 25                                 Mahavir Nagar   Kandivali West,Western Suburbs  19.211319  72.842737
 26                                Thakur village   Kandivali East,Western Suburbs  19.210206   72.87298
 27                                     Pali Naka             Khar,Western Suburbs  19.062742  72.829396
 28                                    Khar Danda             Khar,Western Suburbs  19.068598  72.840042
 29                                      Dindoshi            Malad,Western Suburbs  19.176382  72.864891
 30                                  Sunder Nagar            Malad,Western Suburbs     19.175     72.842
 31                                        Kalina       Sanctacruz,Western Suburbs  19.081667  72.841389
 32                                       Naigaon            Vasai,Western Suburbs  19.351467  72.846343
 33                                    Nalasopara            Vasai,Western Suburbs    19.4154    72.8613
 34                                         Virar                  Western Suburbs      19.47       72.8
 35                                          Irla       Vile Parle,Western Suburbs  19.108056  72.838056
 36                                    Vile Parle                  Western Suburbs       19.1      72.83
 37                                       Bhandup                  Eastern Suburbs      19.14      72.93
 38                                   Amrut Nagar        Ghatkopar,Eastern Suburbs  19.102077  72.912835
 39                                        Asalfa        Ghatkopar,Eastern Suburbs     19.091     72.901
 40                                    Pant Nagar        Ghatkopar,Eastern Suburbs      19.08      72.91
 41                                    Kanjurmarg                  Eastern Suburbs      19.13      72.94
 42                                   Nehru Nagar            Kurla,Eastern Suburbs  15.451686  74.971977
 43                                         Nahur           Mulund,Eastern Suburbs     19.157     72.941
 44                                    Chandivali            Powai,Eastern Suburbs      19.11       72.9
 45                           Hiranandani Gardens            Powai,Eastern Suburbs  19.118986  72.911767
 46  Indian Institute of Technology Bombay campus            Powai,Eastern Suburbs  19.133636  72.915358
 47                                    Vidyavihar                  Eastern Suburbs      19.08     72.896
 48                                      Vikhroli                  Eastern Suburbs      19.11      72.94
 49                                       Chembur                  Harbour Suburbs     19.051     72.894
 50                                        Deonar          Govandi,Harbour Suburbs      19.05      72.89
 51                                      Mankhurd                  Harbour Suburbs      19.05      72.93
 52                                         Mahul          Trombay,Harbour Suburbs         19  72.883333
 53                                      Agripada                     South Mumbai    18.9777    72.8273
 54                                Altamount Road                     South Mumbai    18.9681    72.8095
 55                                    Bhuleshwar                     South Mumbai      18.95      72.83
 56                                  Breach Candy                     South Mumbai     18.967     72.805
 57                               Carmichael Road                     South Mumbai    18.9722    72.8113
 58                                         Cavel                     South Mumbai    18.9474    72.8272
 59                                    Churchgate                     South Mumbai      18.93      72.82
 60                                  Cotton Green                     South Mumbai  18.986209  72.844076
 61                                  Cuffe Parade                     South Mumbai      18.91      72.81
 62                                  Cumbala Hill                     South Mumbai  18.965833  72.805833
 63                                   Currey Road                     South Mumbai     18.994     72.833
 64                                    Dhobitalao                     South Mumbai    18.9433    72.8286
 65                                        Dongri                     South Mumbai  19.283333  72.783333
 66                                    Kala Ghoda                     South Mumbai    18.9307    72.8331
 67                                  Kemps Corner                     South Mumbai    18.9629    72.8054
 68                                   Lower Parel                     South Mumbai  18.995278      72.83
 69                                     Mahalaxmi                     South Mumbai  18.983333       72.8
 70                                         Mahim                     South Mumbai     19.035      72.84
 71                                  Malabar Hill                     South Mumbai      18.95     72.795
 72                                  Marine Drive                     South Mumbai     18.944     72.823
 73                                  Marine Lines                     South Mumbai    18.9447    72.8244
 74                                Mumbai Central                     South Mumbai    18.9697    72.8194
 75                                 Nariman Point                     South Mumbai     18.926     72.823
 76                                    Prabhadevi                     South Mumbai    19.0166    72.8295
 77                                          Sion                     South Mumbai      19.04      72.86
 78                                    Walkeshwar                     South Mumbai  18.947596  72.795957
 79                                         Worli                     South Mumbai         19     72.815
 80                                 C.G.S. colony          Antop Hill,South Mumbai  19.016378  72.856629
 81                                   Dagdi Chawl             Byculla,South Mumbai  18.977129  72.829131
 82                                    Navy Nagar              Colaba,South Mumbai    18.9012    72.8101
 83                                  Hindu colony               Dadar,South Mumbai  19.020841  19.020841
 84                                Ballard Estate                Fort,South Mumbai      18.95      72.84
 85                                  Chira Bazaar           Kalbadevi,South Mumbai   18.94814  72.825462
 86                                    Fanas Wadi           Kalbadevi,South Mumbai  18.951811  72.825309
 87                                   Chor Bazaar         Kamathipura,South Mumbai  18.960321  72.827176
 88                                       Matunga                     South Mumbai   19.01798  72.844763
 89                                         Parel                     South Mumbai      18.99      72.84
 90                                  Gowalia Tank              Tardeo,South Mumbai   18.96245  72.809703
 91                                   Dava Bazaar                     South Mumbai  18.946882  72.831362
 92                                       Dharavi                           Mumbai  19.040208   72.85085
 93                                         Thane                           Mumbai       19.2      72.97]

#to convert data into dataframe, first need to convert into nummpy array,then tranpose the list
df2_T0 = np.array(df1[0][0]).T.tolist()
df2_T1 = np.array(df1[0][1]).T.tolist()
df2_T2 = np.array(df1[0][2]).T.tolist()
df2_T3 = np.array(df1[0][3]).T.tolist()
#noq combine the list and convert into a dataframe
df3=pd.DataFrame(list(zip(df2_T0, df2_T1,df2_T2,df2_T3)),
             columns=['Area','Location','Latitude','Longitude'])
type(df3)

pandas.core.frame.DataFrame

MumbaiLatLan=df3.drop(df3.index[0])
MumbaiLatLan

	Area	Location	Latitude	Longitude
1	Amboli	Andheri,Western Suburbs	19.1293	72.8434
2	Chakala, Andheri	Western Suburbs	19.111388	72.860833
3	D.N. Nagar	Andheri,Western Suburbs	19.124085	72.831373
4	Four Bungalows	Andheri,Western Suburbs	19.124714	72.82721
5	Lokhandwala	Andheri,Western Suburbs	19.130815	72.82927
6	Marol	Andheri,Western Suburbs	19.119219	72.882743
7	Sahar	Andheri,Western Suburbs	19.098889	72.867222
8	Seven Bungalows	Andheri,Western Suburbs	19.129052	72.817018
9	Versova	Andheri,Western Suburbs	19.12	72.82
10	Mira Road	Mira-Bhayandar,Western Suburbs	19.284167	72.871111
11	Bhayandar	Mira-Bhayandar,Western Suburbs	19.29	72.85
12	Uttan	Mira-Bhayandar,Western Suburbs	19.28	72.785
13	Bandstand Promenade	Bandra,Western Suburbs	19.042718	72.819132
14	Kherwadi	Bandra,Western Suburbs	19.0553	72.8314
15	Pali Hill	Bandra,Western Suburbs	19.068	72.826
16	I.C. Colony	Borivali (West),Western Suburbs	19.247039	72.84983
17	Gorai	Borivali (West),Western Suburbs	19.250057	72.782021
18	Dahisa	Western Suburbs	19.250069	72.859347
19	Aarey Milk Colony	Goregaon,Western Suburbs	19.148493	72.881756
20	Bangur Nagar	Goregaon,Western Suburbs	19.167362	72.832252
21	Jogeshwari West	Western Suburbs	19.12	72.85
22	Juhu	Western Suburbs	19.1	72.83
23	Charkop	Kandivali West,Western Suburbs	19.216182	72.830575
24	Poisar	Kandivali West,Western Suburbs	19.204511	72.837639
25	Mahavir Nagar	Kandivali West,Western Suburbs	19.211319	72.842737
26	Thakur village	Kandivali East,Western Suburbs	19.210206	72.87298
27	Pali Naka	Khar,Western Suburbs	19.062742	72.829396
28	Khar Danda	Khar,Western Suburbs	19.068598	72.840042
29	Dindoshi	Malad,Western Suburbs	19.176382	72.864891
30	Sunder Nagar	Malad,Western Suburbs	19.175	72.842
31	Kalina	Sanctacruz,Western Suburbs	19.081667	72.841389
32	Naigaon	Vasai,Western Suburbs	19.351467	72.846343
33	Nalasopara	Vasai,Western Suburbs	19.4154	72.8613
34	Virar	Western Suburbs	19.47	72.8
35	Irla	Vile Parle,Western Suburbs	19.108056	72.838056
36	Vile Parle	Western Suburbs	19.1	72.83
37	Bhandup	Eastern Suburbs	19.14	72.93
38	Amrut Nagar	Ghatkopar,Eastern Suburbs	19.102077	72.912835
39	Asalfa	Ghatkopar,Eastern Suburbs	19.091	72.901
40	Pant Nagar	Ghatkopar,Eastern Suburbs	19.08	72.91
41	Kanjurmarg	Eastern Suburbs	19.13	72.94
42	Nehru Nagar	Kurla,Eastern Suburbs	15.451686	74.971977
43	Nahur	Mulund,Eastern Suburbs	19.157	72.941
44	Chandivali	Powai,Eastern Suburbs	19.11	72.9
45	Hiranandani Gardens	Powai,Eastern Suburbs	19.118986	72.911767
46	Indian Institute of Technology Bombay campus	Powai,Eastern Suburbs	19.133636	72.915358
47	Vidyavihar	Eastern Suburbs	19.08	72.896
48	Vikhroli	Eastern Suburbs	19.11	72.94
49	Chembur	Harbour Suburbs	19.051	72.894
50	Deonar	Govandi,Harbour Suburbs	19.05	72.89
51	Mankhurd	Harbour Suburbs	19.05	72.93
52	Mahul	Trombay,Harbour Suburbs	19	72.883333
53	Agripada	South Mumbai	18.9777	72.8273
54	Altamount Road	South Mumbai	18.9681	72.8095
55	Bhuleshwar	South Mumbai	18.95	72.83
56	Breach Candy	South Mumbai	18.967	72.805
57	Carmichael Road	South Mumbai	18.9722	72.8113
58	Cavel	South Mumbai	18.9474	72.8272
59	Churchgate	South Mumbai	18.93	72.82
60	Cotton Green	South Mumbai	18.986209	72.844076
61	Cuffe Parade	South Mumbai	18.91	72.81
62	Cumbala Hill	South Mumbai	18.965833	72.805833
63	Currey Road	South Mumbai	18.994	72.833
64	Dhobitalao	South Mumbai	18.9433	72.8286
65	Dongri	South Mumbai	19.283333	72.783333
66	Kala Ghoda	South Mumbai	18.9307	72.8331
67	Kemps Corner	South Mumbai	18.9629	72.8054
68	Lower Parel	South Mumbai	18.995278	72.83
69	Mahalaxmi	South Mumbai	18.983333	72.8
70	Mahim	South Mumbai	19.035	72.84
71	Malabar Hill	South Mumbai	18.95	72.795
72	Marine Drive	South Mumbai	18.944	72.823
73	Marine Lines	South Mumbai	18.9447	72.8244
74	Mumbai Central	South Mumbai	18.9697	72.8194
75	Nariman Point	South Mumbai	18.926	72.823
76	Prabhadevi	South Mumbai	19.0166	72.8295
77	Sion	South Mumbai	19.04	72.86
78	Walkeshwar	South Mumbai	18.947596	72.795957
79	Worli	South Mumbai	19	72.815
80	C.G.S. colony	Antop Hill,South Mumbai	19.016378	72.856629
81	Dagdi Chawl	Byculla,South Mumbai	18.977129	72.829131
82	Navy Nagar	Colaba,South Mumbai	18.9012	72.8101
83	Hindu colony	Dadar,South Mumbai	19.020841	19.020841
84	Ballard Estate	Fort,South Mumbai	18.95	72.84
85	Chira Bazaar	Kalbadevi,South Mumbai	18.94814	72.825462
86	Fanas Wadi	Kalbadevi,South Mumbai	18.951811	72.825309
87	Chor Bazaar	Kamathipura,South Mumbai	18.960321	72.827176
88	Matunga	South Mumbai	19.01798	72.844763
89	Parel	South Mumbai	18.99	72.84
90	Gowalia Tank	Tardeo,South Mumbai	18.96245	72.809703
91	Dava Bazaar	South Mumbai	18.946882	72.831362
92	Dharavi	Mumbai	19.040208	72.85085
93	Thane	Mumbai	19.2	72.97

# create map of Mumbai using latitude and longitude values
map_Mumbai = folium.Map(location=[latitude, longitude], zoom_start=11)
# add markers to map

map_Mumbai

Make this Notebook Trusted to load map: File -> Trust Notebook

# create map of Mumbai using latitude and longitude values
map_mumbai = folium.Map(location=[latitude, longitude], zoom_start=11)
# add markers to map
for lat, lng, label in zip(MumbaiLatLan['Latitude'], MumbaiLatLan['Longitude'],MumbaiLatLan['Area']):
    label = folium.Popup(label, parse_html=False)
    folium.CircleMarker(
        [lat, lng],
        radius=5,
        popup=label,
        color='blue',
        fill=True,
        fill_color='#3186cc',
        fill_opacity=1,
        parse_html=False).add_to(map_mumbai)  
map_mumbai

Make this Notebook Trusted to load map: File -> Trust Notebook

#Define Foursquare Credentials and Version
#@hidden_cell
CLIENT_ID = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX' # your Foursquare ID
CLIENT_SECRET = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX' # your Foursquare Secret
VERSION = '20180605' # Foursquare API version
LIMIT = 100 # A default Foursquare API limit value

#Explore Neighborhoods in Mumbai
def getNearbyVenues(names, latitudes, longitudes, radius=2500):
    
    venues_list=[]
    for name, lat, lng in zip(names, latitudes, longitudes):
        print(name)
            
        # create the API request URL
        url = 'https://api.foursquare.com/v2/venues/explore?&client_id={}&client_secret={}&v={}&ll={},{}&radius={}&limit={}'.format(
            CLIENT_ID, 
            CLIENT_SECRET, 
            VERSION, 
            lat, 
            lng, 
            radius, 
            LIMIT)
            
        # make the GET request
        results = requests.get(url).json()["response"]['groups'][0]['items']
        
        # return only relevant information for each nearby venue
        venues_list.append([(
            name, 
            lat, 
            lng, 
            v['venue']['name'], 
            v['venue']['location']['lat'], 
            v['venue']['location']['lng'],  
            v['venue']['categories'][0]['name']) for v in results])

    nearby_venues = pd.DataFrame([item for venue_list in venues_list for item in venue_list])
    nearby_venues.columns = ['Neighborhood', 
                  'Neighborhood Latitude', 
                  'Neighborhood Longitude', 
                  'Venue', 
                  'Venue Latitude', 
                  'Venue Longitude', 
                  'Venue Category']
    
    return(nearby_venues)

#Now write the code to run the above function on each neighborhood and create a new dataframe called Mumbai_venues.
# type your answer here
Mumbai_venues = getNearbyVenues(names=MumbaiLatLan['Area'],
                                   latitudes=MumbaiLatLan['Latitude'],
                                   longitudes=MumbaiLatLan['Longitude']
                                  )

print(Mumbai_venues.shape)
Mumbai_venues.head(5)

Amboli
Chakala, Andheri
D.N. Nagar
Four Bungalows
Lokhandwala
Marol
Sahar
Seven Bungalows
Versova
Mira Road
Bhayandar
Uttan
Bandstand Promenade
Kherwadi
Pali Hill
I.C. Colony
Gorai
Dahisa
Aarey Milk Colony
Bangur Nagar
Jogeshwari West
Juhu
Charkop
Poisar
Mahavir Nagar
Thakur village
Pali Naka
Khar Danda
Dindoshi
Sunder Nagar
Kalina
Naigaon
Nalasopara
Virar
Irla
Vile Parle
Bhandup
Amrut Nagar
Asalfa
Pant Nagar
Kanjurmarg
Nehru Nagar
Nahur
Chandivali
Hiranandani Gardens
Indian Institute of Technology Bombay campus
Vidyavihar
Vikhroli
Chembur
Deonar
Mankhurd
Mahul
Agripada
Altamount Road
Bhuleshwar
Breach Candy
Carmichael Road
Cavel
Churchgate
Cotton Green
Cuffe Parade
Cumbala Hill
Currey Road
Dhobitalao
Dongri
Kala Ghoda
Kemps Corner
Lower Parel
Mahalaxmi
Mahim
Malabar Hill
Marine Drive
Marine Lines
Mumbai Central
Nariman Point
Prabhadevi
Sion
Walkeshwar
Worli
C.G.S. colony
Dagdi Chawl
Navy Nagar
Hindu colony
Ballard Estate
Chira Bazaar
Fanas Wadi
Chor Bazaar
Matunga
Parel
Gowalia Tank
Dava Bazaar
Dharavi
Thane
(7371, 7)

	Neighborhood	Neighborhood Latitude	Neighborhood Longitude	Venue	Venue Latitude	Venue Longitude	Venue Category
0	Amboli	19.1293	72.8434	Merwans Cake shop	19.119300	72.845418	Bakery
1	Amboli	19.1293	72.8434	Jaffer Bhai's Delhi Darbar	19.137714	72.845909	Mughlai Restaurant
2	Amboli	19.1293	72.8434	Hard Rock Cafe Andheri	19.135995	72.835335	American Restaurant
3	Amboli	19.1293	72.8434	Shawarma Factory	19.124591	72.840398	Falafel Restaurant
4	Amboli	19.1293	72.8434	Joey's Pizza	19.126762	72.830001	Pizza Place

#Let's check how many venues were returned for each neighborhood
Mumbai_venues.groupby('Neighborhood').count()

	Neighborhood Latitude	Neighborhood Longitude	Venue	Venue Latitude	Venue Longitude	Venue Category
Neighborhood
Aarey Milk Colony	25	25	25	25	25	25
Agripada	100	100	100	100	100	100
Altamount Road	100	100	100	100	100	100
Amboli	100	100	100	100	100	100
Amrut Nagar	100	100	100	100	100	100
Asalfa	100	100	100	100	100	100
Ballard Estate	100	100	100	100	100	100
Bandstand Promenade	100	100	100	100	100	100
Bangur Nagar	100	100	100	100	100	100
Bhandup	41	41	41	41	41	41
Bhayandar	28	28	28	28	28	28
Bhuleshwar	100	100	100	100	100	100
Breach Candy	100	100	100	100	100	100
C.G.S. colony	92	92	92	92	92	92
Carmichael Road	100	100	100	100	100	100
Cavel	100	100	100	100	100	100
Chakala, Andheri	100	100	100	100	100	100
Chandivali	100	100	100	100	100	100
Charkop	92	92	92	92	92	92
Chembur	82	82	82	82	82	82
Chira Bazaar	100	100	100	100	100	100
Chor Bazaar	56	56	56	56	56	56
Churchgate	100	100	100	100	100	100
Cotton Green	100	100	100	100	100	100
Cuffe Parade	65	65	65	65	65	65
Cumbala Hill	100	100	100	100	100	100
Currey Road	100	100	100	100	100	100
D.N. Nagar	100	100	100	100	100	100
Dagdi Chawl	100	100	100	100	100	100
Dahisa	75	75	75	75	75	75
Dava Bazaar	100	100	100	100	100	100
Deonar	86	86	86	86	86	86
Dharavi	100	100	100	100	100	100
Dhobitalao	100	100	100	100	100	100
Dindoshi	72	72	72	72	72	72
Dongri	9	9	9	9	9	9
Fanas Wadi	100	100	100	100	100	100
Four Bungalows	100	100	100	100	100	100
Gorai	5	5	5	5	5	5
Gowalia Tank	100	100	100	100	100	100
Hiranandani Gardens	100	100	100	100	100	100
I.C. Colony	88	88	88	88	88	88
Indian Institute of Technology Bombay campus	91	91	91	91	91	91
Irla	100	100	100	100	100	100
Jogeshwari West	100	100	100	100	100	100
Juhu	69	69	69	69	69	69
Kala Ghoda	100	100	100	100	100	100
Kalina	100	100	100	100	100	100
Kanjurmarg	28	28	28	28	28	28
Kemps Corner	99	99	99	99	99	99
Khar Danda	100	100	100	100	100	100
Kherwadi	57	57	57	57	57	57
Lokhandwala	100	100	100	100	100	100
Lower Parel	100	100	100	100	100	100
Mahalaxmi	65	65	65	65	65	65
Mahavir Nagar	100	100	100	100	100	100
Mahim	100	100	100	100	100	100
Mahul	4	4	4	4	4	4
Malabar Hill	61	61	61	61	61	61
Mankhurd	13	13	13	13	13	13
Marine Drive	100	100	100	100	100	100
Marine Lines	100	100	100	100	100	100
Marol	100	100	100	100	100	100
Matunga	100	100	100	100	100	100
Mira Road	37	37	37	37	37	37
Mumbai Central	100	100	100	100	100	100
Nahur	71	71	71	71	71	71
Naigaon	4	4	4	4	4	4
Nalasopara	7	7	7	7	7	7
Nariman Point	100	100	100	100	100	100
Navy Nagar	23	23	23	23	23	23
Nehru Nagar	6	6	6	6	6	6
Pali Hill	100	100	100	100	100	100
Pali Naka	65	65	65	65	65	65
Pant Nagar	100	100	100	100	100	100
Parel	100	100	100	100	100	100
Poisar	100	100	100	100	100	100
Prabhadevi	100	100	100	100	100	100
Sahar	100	100	100	100	100	100
Seven Bungalows	100	100	100	100	100	100
Sion	100	100	100	100	100	100
Sunder Nagar	100	100	100	100	100	100
Thakur village	75	75	75	75	75	75
Thane	100	100	100	100	100	100
Uttan	8	8	8	8	8	8
Versova	100	100	100	100	100	100
Vidyavihar	100	100	100	100	100	100
Vikhroli	24	24	24	24	24	24
Vile Parle	69	69	69	69	69	69
Virar	14	14	14	14	14	14
Walkeshwar	65	65	65	65	65	65
Worli	100	100	100	100	100	100

#Let's find out how many unique categories can be curated from all the returned venues
print('There are {} uniques categories.'.format(len(Mumbai_venues['Venue Category'].unique())))

There are 210 uniques categories.

# Now just keeing Shopping malls & Theatre for the analysis
Mumbai_venues_2=Mumbai_venues[(Mumbai_venues['Venue Category']=='Shopping Mall') | (Mumbai_venues['Venue Category']=='Movie Theater') 
                           | (Mumbai_venues['Venue Category']=='Theater') | (Mumbai_venues['Venue Category']=='Indie Movie Theater')]

Mumbai_venues_2['Venue Category'] = Mumbai_venues_2['Venue Category'].replace(['Movie Theater','Indie Movie Theater'],'Theater')

Mumbai_venues_2

C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  """

	Neighborhood	Neighborhood Latitude	Neighborhood Longitude	Venue	Venue Latitude	Venue Longitude	Venue Category
28	Amboli	19.1293	72.8434	PVR ICON	19.141531	72.830331	Theater
229	D.N. Nagar	19.124085	72.831373	Prithvi Theatre	19.106157	72.825810	Theater
255	D.N. Nagar	19.124085	72.831373	PVR ICON	19.141531	72.830331	Theater
330	Four Bungalows	19.124714	72.82721	Prithvi Theatre	19.106157	72.825810	Theater
352	Four Bungalows	19.124714	72.82721	PVR ICON	19.141531	72.830331	Theater
423	Lokhandwala	19.130815	72.82927	PVR ICON	19.141531	72.830331	Theater
749	Seven Bungalows	19.129052	72.817018	PVR ICON	19.141531	72.830331	Theater
812	Versova	19.12	72.82	Prithvi Theatre	19.106157	72.825810	Theater
932	Mira Road	19.284167	72.871111	Inox Cinemas - Thakur Mall - Dahisar	19.263130	72.874483	Theater
1046	Bandstand Promenade	19.042718	72.819132	Le Rêve (Globus Cinema)	19.056014	72.833951	Theater
1257	I.C. Colony	19.247039	72.84983	Sona GOLD	19.234830	72.865032	Theater
1290	I.C. Colony	19.247039	72.84983	Prabodhankar Thackeray Natya Mandir	19.233363	72.854462	Theater
1336	Dahisa	19.250069	72.859347	Sona GOLD	19.234830	72.865032	Theater
1378	Dahisa	19.250069	72.859347	Prabodhankar Thackeray Natya Mandir	19.233363	72.854462	Theater
1395	Dahisa	19.250069	72.859347	Inox Cinemas - Thakur Mall - Dahisar	19.263130	72.874483	Theater
1427	Bangur Nagar	19.167362	72.832252	Inorbit Mall	19.173065	72.835755	Shopping Mall
1437	Bangur Nagar	19.167362	72.832252	Infiniti Mall	19.184692	72.834914	Shopping Mall
1629	Juhu	19.1	72.83	Prithvi Theatre	19.106157	72.825810	Theater
1771	Charkop	19.216182	72.830575	INOX Raghuleela	19.213397	72.849115	Theater
1803	Poisar	19.204511	72.837639	Infiniti Mall	19.184692	72.834914	Shopping Mall
1862	Poisar	19.204511	72.837639	Growel's 101	19.202952	72.860453	Shopping Mall
1930	Mahavir Nagar	19.211319	72.842737	Growel's 101	19.202952	72.860453	Shopping Mall
1997	Thakur village	19.210206	72.87298	Inox	19.212185	72.871501	Theater
2004	Thakur village	19.210206	72.87298	Growel's 101	19.202952	72.860453	Shopping Mall
2032	Thakur village	19.210206	72.87298	Metro Mall	19.218714	72.866107	Shopping Mall
2049	Thakur village	19.210206	72.87298	Centrium Mall	19.196567	72.871714	Shopping Mall
2229	Dindoshi	19.176382	72.864891	Oberoi Mall	19.174061	72.860412	Shopping Mall
2300	Sunder Nagar	19.175	72.842	Infiniti Mall	19.184692	72.834914	Shopping Mall
2308	Sunder Nagar	19.175	72.842	Inorbit Mall	19.173065	72.835755	Shopping Mall
2332	Sunder Nagar	19.175	72.842	Oberoi Mall	19.174061	72.860412	Shopping Mall
2527	Irla	19.108056	72.838056	Prithvi Theatre	19.106157	72.825810	Theater
2627	Vile Parle	19.1	72.83	Prithvi Theatre	19.106157	72.825810	Theater
2708	Bhandup	19.14	72.93	Magnet Mall	19.141503	72.931681	Shopping Mall
2733	Amrut Nagar	19.102077	72.912835	R City Mall	19.099320	72.916714	Shopping Mall
2826	Amrut Nagar	19.102077	72.912835	Neptune Magnet Mall	19.113237	72.916708	Shopping Mall
2833	Asalfa	19.091	72.901	PVR Cinemas	19.086643	72.889839	Theater
2842	Asalfa	19.091	72.901	Phoenix Marketcity	19.085914	72.888692	Shopping Mall
2846	Asalfa	19.091	72.901	R City Mall	19.099320	72.916714	Shopping Mall
2947	Pant Nagar	19.08	72.91	PVR Cinemas	19.086643	72.889839	Theater
2951	Pant Nagar	19.08	72.91	R City Mall	19.099320	72.916714	Shopping Mall
2961	Pant Nagar	19.08	72.91	Phoenix Marketcity	19.085914	72.888692	Shopping Mall
3049	Kanjurmarg	19.13	72.94	Magnet Mall	19.141503	72.931681	Shopping Mall
3083	Nahur	19.157	72.941	R mall, Mulund west	19.175375	72.950506	Shopping Mall
3096	Nahur	19.157	72.941	Mahakavi Kalidas Natya Mandir	19.173408	72.949012	Theater
3124	Nahur	19.157	72.941	Magnet Mall	19.141503	72.931681	Shopping Mall
3162	Chandivali	19.11	72.9	R City Mall	19.099320	72.916714	Shopping Mall
3278	Hiranandani Gardens	19.118986	72.911767	R City Mall	19.099320	72.916714	Shopping Mall
3410	Indian Institute of Technology Bombay campus	19.133636	72.915358	Magnet Mall	19.141503	72.931681	Shopping Mall
3413	Indian Institute of Technology Bombay campus	19.133636	72.915358	Huma Mall	19.134935	72.927979	Shopping Mall
3428	Vidyavihar	19.08	72.896	PVR Cinemas	19.086643	72.889839	Theater
3434	Vidyavihar	19.08	72.896	Phoenix Marketcity	19.085914	72.888692	Shopping Mall
3514	Vidyavihar	19.08	72.896	Cinemax	19.074561	72.906534	Theater
3537	Vikhroli	19.11	72.94	Neptune Magnet Mall	19.113237	72.916708	Shopping Mall
3595	Chembur	19.051	72.894	Movie Time	19.045790	72.903118	Theater
3677	Deonar	19.05	72.89	Movie Time	19.045790	72.903118	Theater
3747	Agripada	18.9777	72.8273	High Street Phoenix	18.994967	72.825032	Shopping Mall
3752	Agripada	18.9777	72.8273	Palladium	18.994317	72.824398	Shopping Mall
3833	Agripada	18.9777	72.8273	Sophia Bhabha Hall	18.969819	72.807082	Theater
3849	Altamount Road	18.9681	72.8095	Sophia Bhabha Hall	18.969819	72.807082	Theater
4051	Breach Candy	18.967	72.805	Sophia Bhabha Hall	18.969819	72.807082	Theater
4152	Carmichael Road	18.9722	72.8113	Sophia Bhabha Hall	18.969819	72.807082	Theater
4232	Carmichael Road	18.9722	72.8113	Nehru Auditorium	18.988726	72.814814	Theater
4342	Churchgate	18.93	72.82	Tata Theatre	18.925063	72.819958	Theater
4357	Churchgate	18.93	72.82	Jamshed Bhabha Theatre	18.925134	72.819730	Theater
4386	Churchgate	18.93	72.82	INOX	18.926465	72.822470	Theater
4397	Churchgate	18.93	72.82	Experimental Theatre	18.926086	72.820249	Theater
4440	Cotton Green	18.986209	72.844076	High Street Phoenix	18.994967	72.825032	Shopping Mall
4447	Cotton Green	18.986209	72.844076	Palladium	18.994317	72.824398	Shopping Mall
4541	Cuffe Parade	18.91	72.81	Tata Theatre	18.925063	72.819958	Theater
4554	Cuffe Parade	18.91	72.81	Jamshed Bhabha Theatre	18.925134	72.819730	Theater
4571	Cuffe Parade	18.91	72.81	INOX	18.926465	72.822470	Theater
4573	Cuffe Parade	18.91	72.81	Experimental Theatre	18.926086	72.820249	Theater
4579	Cuffe Parade	18.91	72.81	Maker Arcade	18.916494	72.817852	Shopping Mall
4620	Cumbala Hill	18.965833	72.805833	Sophia Bhabha Hall	18.969819	72.807082	Theater
4701	Currey Road	18.994	72.833	High Street Phoenix	18.994967	72.825032	Shopping Mall
4709	Currey Road	18.994	72.833	Palladium	18.994317	72.824398	Shopping Mall
4763	Currey Road	18.994	72.833	Matterden Centre	19.006067	72.830822	Theater
4880	Dhobitalao	18.9433	72.8286	Tata Theatre	18.925063	72.819958	Theater
4978	Kala Ghoda	18.9307	72.8331	Tata Theatre	18.925063	72.819958	Theater
4999	Kala Ghoda	18.9307	72.8331	Jamshed Bhabha Theatre	18.925134	72.819730	Theater
5008	Kala Ghoda	18.9307	72.8331	INOX	18.926465	72.822470	Theater
5040	Kemps Corner	18.9629	72.8054	Sophia Bhabha Hall	18.969819	72.807082	Theater
5109	Lower Parel	18.995278	72.83	High Street Phoenix	18.994967	72.825032	Shopping Mall
5112	Lower Parel	18.995278	72.83	Palladium	18.994317	72.824398	Shopping Mall
5172	Lower Parel	18.995278	72.83	Matterden Centre	19.006067	72.830822	Theater
5236	Mahalaxmi	18.983333	72.8	Sophia Bhabha Hall	18.969819	72.807082	Theater
5316	Mahim	19.035	72.84	Citylight Theatre	19.030302	72.841627	Theater
5349	Mahim	19.035	72.84	Shivaji Mandir	19.022554	72.842050	Theater
5370	Mahim	19.035	72.84	Le Rêve (Globus Cinema)	19.056014	72.833951	Theater
5492	Marine Drive	18.944	72.823	Tata Theatre	18.925063	72.819958	Theater
5601	Marine Lines	18.9447	72.8244	Tata Theatre	18.925063	72.819958	Theater
5678	Mumbai Central	18.9697	72.8194	Sophia Bhabha Hall	18.969819	72.807082	Theater
5739	Nariman Point	18.926	72.823	Tata Theatre	18.925063	72.819958	Theater
5749	Nariman Point	18.926	72.823	Jamshed Bhabha Theatre	18.925134	72.819730	Theater
5775	Nariman Point	18.926	72.823	INOX	18.926465	72.822470	Theater
5810	Nariman Point	18.926	72.823	Experimental Theatre	18.926086	72.820249	Theater
5863	Prabhadevi	19.0166	72.8295	Matterden Centre	19.006067	72.830822	Theater
5872	Prabhadevi	19.0166	72.8295	High Street Phoenix	18.994967	72.825032	Shopping Mall
5899	Prabhadevi	19.0166	72.8295	Shivaji Mandir	19.022554	72.842050	Theater
5927	Prabhadevi	19.0166	72.8295	Citylight Theatre	19.030302	72.841627	Theater
6006	Sion	19.04	72.86	Citylight Theatre	19.030302	72.841627	Theater
6030	Sion	19.04	72.86	Aurora Cinema	19.028134	72.857326	Theater
6105	Worli	19	72.815	High Street Phoenix	18.994967	72.825032	Shopping Mall
6107	Worli	19	72.815	Palladium	18.994317	72.824398	Shopping Mall
6165	Worli	19	72.815	Matterden Centre	19.006067	72.830822	Theater
6243	C.G.S. colony	19.016378	72.856629	Shivaji Mandir	19.022554	72.842050	Theater
6246	C.G.S. colony	19.016378	72.856629	Big cinemas	19.010945	72.855734	Theater
6266	C.G.S. colony	19.016378	72.856629	Citylight Theatre	19.030302	72.841627	Theater
6275	C.G.S. colony	19.016378	72.856629	Aurora Cinema	19.028134	72.857326	Theater
6284	C.G.S. colony	19.016378	72.856629	Iifa	19.027471	72.853725	Shopping Mall
6308	Dagdi Chawl	18.977129	72.829131	High Street Phoenix	18.994967	72.825032	Shopping Mall
6312	Dagdi Chawl	18.977129	72.829131	Palladium	18.994317	72.824398	Shopping Mall
6390	Dagdi Chawl	18.977129	72.829131	Sophia Bhabha Hall	18.969819	72.807082	Theater
6406	Navy Nagar	18.9012	72.8101	Maker Arcade	18.916494	72.817852	Shopping Mall
6767	Chor Bazaar	18.960321	72.827176	Liberty cinema	18.941652	72.827389	Theater
6802	Matunga	19.01798	72.844763	Shivaji Mandir	19.022554	72.842050	Theater
6833	Matunga	19.01798	72.844763	Matterden Centre	19.006067	72.830822	Theater
6852	Matunga	19.01798	72.844763	Citylight Theatre	19.030302	72.841627	Theater
6874	Parel	18.99	72.84	High Street Phoenix	18.994967	72.825032	Shopping Mall
6881	Parel	18.99	72.84	Palladium	18.994317	72.824398	Shopping Mall
6935	Parel	18.99	72.84	Matterden Centre	19.006067	72.830822	Theater
7008	Gowalia Tank	18.96245	72.809703	Sophia Bhabha Hall	18.969819	72.807082	Theater
7228	Dharavi	19.040208	72.85085	Citylight Theatre	19.030302	72.841627	Theater
7242	Dharavi	19.040208	72.85085	Shivaji Mandir	19.022554	72.842050	Theater
7244	Dharavi	19.040208	72.85085	Le Rêve (Globus Cinema)	19.056014	72.833951	Theater
7274	Thane	19.2	72.97	Viviana	19.208302	72.971991	Shopping Mall
7277	Thane	19.2	72.97	Korum Mall	19.202400	72.965802	Shopping Mall
7286	Thane	19.2	72.97	Gadkari Rangayatan	19.194063	72.973839	Theater
7370	Thane	19.2	72.97	High Street Mall	19.217357	72.980912	Shopping Mall

#3. Analyze Each Neighborhood
# one hot encoding
Mumbai_onehot = pd.get_dummies(Mumbai_venues_2[['Venue Category']], prefix="", prefix_sep="")
Mumbai_onehot

	Shopping Mall	Theater
28	0	1
229	0	1
255	0	1
330	0	1
352	0	1
423	0	1
749	0	1
812	0	1
932	0	1
1046	0	1
1257	0	1
1290	0	1
1336	0	1
1378	0	1
1395	0	1
1427	1	0
1437	1	0
1629	0	1
1771	0	1
1803	1	0
1862	1	0
1930	1	0
1997	0	1
2004	1	0
2032	1	0
2049	1	0
2229	1	0
2300	1	0
2308	1	0
2332	1	0
2527	0	1
2627	0	1
2708	1	0
2733	1	0
2826	1	0
2833	0	1
2842	1	0
2846	1	0
2947	0	1
2951	1	0
2961	1	0
3049	1	0
3083	1	0
3096	0	1
3124	1	0
3162	1	0
3278	1	0
3410	1	0
3413	1	0
3428	0	1
3434	1	0
3514	0	1
3537	1	0
3595	0	1
3677	0	1
3747	1	0
3752	1	0
3833	0	1
3849	0	1
4051	0	1
4152	0	1
4232	0	1
4342	0	1
4357	0	1
4386	0	1
4397	0	1
4440	1	0
4447	1	0
4541	0	1
4554	0	1
4571	0	1
4573	0	1
4579	1	0
4620	0	1
4701	1	0
4709	1	0
4763	0	1
4880	0	1
4978	0	1
4999	0	1
5008	0	1
5040	0	1
5109	1	0
5112	1	0
5172	0	1
5236	0	1
5316	0	1
5349	0	1
5370	0	1
5492	0	1
5601	0	1
5678	0	1
5739	0	1
5749	0	1
5775	0	1
5810	0	1
5863	0	1
5872	1	0
5899	0	1
5927	0	1
6006	0	1
6030	0	1
6105	1	0
6107	1	0
6165	0	1
6243	0	1
6246	0	1
6266	0	1
6275	0	1
6284	1	0
6308	1	0
6312	1	0
6390	0	1
6406	1	0
6767	0	1
6802	0	1
6833	0	1
6852	0	1
6874	1	0
6881	1	0
6935	0	1
7008	0	1
7228	0	1
7242	0	1
7244	0	1
7274	1	0
7277	1	0
7286	0	1
7370	1	0

# add neighborhood column back to dataframe
Mumbai_onehot['Neighborhood'] = Mumbai_venues_2['Neighborhood'] 

# move neighborhood column to the first column
fixed_columns = [Mumbai_onehot.columns[-1]] + list(Mumbai_onehot.columns[:-1])
Mumbai_onehot = Mumbai_onehot[fixed_columns]
Mumbai_onehot.head()

	Neighborhood	Shopping Mall	Theater
28	Amboli	0	1
229	D.N. Nagar	0	1
255	D.N. Nagar	0	1
330	Four Bungalows	0	1
352	Four Bungalows	0	1

Mumbai_onehot.shape

(129, 3)

#Next, let's group rows by neighborhood and by taking the mean of the frequency of occurrence of each category
Mumbai_grouped_1 = Mumbai_onehot.groupby('Neighborhood').sum().reset_index()
Mumbai_grouped_2 = Mumbai_onehot.groupby('Neighborhood').mean().reset_index()
Mumbai_grouped= Mumbai_grouped_1.merge(Mumbai_grouped_2,how='inner',left_on=['Neighborhood'],right_on=['Neighborhood'])
Mumbai_grouped
Mumbai_grouped.shape

(64, 5)

Mumbai_grouped.head()

	Neighborhood	Shopping Mall_x	Theater_x	Shopping Mall_y	Theater_y
0	Agripada	2	1	0.666667	0.333333
1	Altamount Road	0	1	0.000000	1.000000
2	Amboli	0	1	0.000000	1.000000
3	Amrut Nagar	2	0	1.000000	0.000000
4	Asalfa	2	1	0.666667	0.333333

#4. Cluster Neighborhoods findout best K
Mumbai_grouped_clustering = Mumbai_grouped_2.drop('Neighborhood', 1)

sil = []
kmax = 10

# dissimilarity would not be defined for a single cluster, thus, minimum number of clusters should be 2
for k in range(2, kmax):
    kmeans = KMeans(n_clusters=k, random_state=1).fit(Mumbai_grouped_clustering)
    labels = kmeans.labels_
    sil.append(silhouette_score(Mumbai_grouped_clustering, labels, metric = 'euclidean'))

sil

[0.8746790609454447,
 0.9278923830070563,
 0.9647569403395169,
 0.9811631761149555,
 0.9691406059845273,
 0.9687499756338532,
 0.9687499756338532,
 0.9687499756338532]

# Plot the elbow
k= range(2,10)
plt.plot(k, sil, 'bx-')
plt.xlabel('k')
plt.ylabel('sil')
plt.title('The Silhouette Method showing the optimal k')
plt.show()

# The best number of clusters with min of cluster value is 6 from above chart
# set number of clusters
kclusters = 5

# run k-means clustering
kmeans = KMeans(n_clusters=kclusters, random_state=1).fit(Mumbai_grouped_clustering)

# check cluster labels generated for each row in the dataframe
kmeans.labels_[0:10] 

array([2, 0, 0, 1, 2, 0, 1, 1, 0, 3])

#Mumbai_grouped_clustering
#Let's create a new dataframe that includes the cluster as well as the top 10 venues for each neighborhood.
# add clustering labels
Mumbai_grouped.insert(0,'Cluster Labels', kmeans.labels_)
Mumbai_merged = MumbaiLatLan

# merge manhattan_grouped with manhattan_data to add latitude/longitude for each neighborhood
Mumbai_merged = Mumbai_merged.merge(Mumbai_grouped,how='inner',left_on=['Area'],right_on=['Neighborhood'])


Mumbai_merged.head() # check the last columns!

	Area	Location	Latitude	Longitude	Cluster Labels	Neighborhood	Shopping Mall_x	Theater_x	Shopping Mall_y	Theater_y
0	Amboli	Andheri,Western Suburbs	19.1293	72.8434	0	Amboli	0	1	0.0	1.0
1	D.N. Nagar	Andheri,Western Suburbs	19.124085	72.831373	0	D.N. Nagar	0	2	0.0	1.0
2	Four Bungalows	Andheri,Western Suburbs	19.124714	72.82721	0	Four Bungalows	0	2	0.0	1.0
3	Lokhandwala	Andheri,Western Suburbs	19.130815	72.82927	0	Lokhandwala	0	1	0.0	1.0
4	Seven Bungalows	Andheri,Western Suburbs	19.129052	72.817018	0	Seven Bungalows	0	1	0.0	1.0

#updaating lables for each cluster
Mumbai_merged['Labels'] = Mumbai_merged['Cluster Labels'].map({0:'Only Theater',1:'Only Shopping Mall'
                                ,2:'66% Shopping Mall/33% Theater',3:'33% Shopping Mall/66% Theater'
                                ,4:'75% Shopping Mall/25% Theater'})
Mumbai_merged

	Area	Location	Latitude	Longitude	Cluster Labels	Neighborhood	Shopping Mall_x	Theater_x	Shopping Mall_y	Theater_y	Labels
0	Amboli	Andheri,Western Suburbs	19.1293	72.8434	0	Amboli	0	1	0.000000	1.000000	Only Theater
1	D.N. Nagar	Andheri,Western Suburbs	19.124085	72.831373	0	D.N. Nagar	0	2	0.000000	1.000000	Only Theater
2	Four Bungalows	Andheri,Western Suburbs	19.124714	72.82721	0	Four Bungalows	0	2	0.000000	1.000000	Only Theater
3	Lokhandwala	Andheri,Western Suburbs	19.130815	72.82927	0	Lokhandwala	0	1	0.000000	1.000000	Only Theater
4	Seven Bungalows	Andheri,Western Suburbs	19.129052	72.817018	0	Seven Bungalows	0	1	0.000000	1.000000	Only Theater
5	Versova	Andheri,Western Suburbs	19.12	72.82	0	Versova	0	1	0.000000	1.000000	Only Theater
6	Mira Road	Mira-Bhayandar,Western Suburbs	19.284167	72.871111	0	Mira Road	0	1	0.000000	1.000000	Only Theater
7	Bandstand Promenade	Bandra,Western Suburbs	19.042718	72.819132	0	Bandstand Promenade	0	1	0.000000	1.000000	Only Theater
8	I.C. Colony	Borivali (West),Western Suburbs	19.247039	72.84983	0	I.C. Colony	0	2	0.000000	1.000000	Only Theater
9	Dahisa	Western Suburbs	19.250069	72.859347	0	Dahisa	0	3	0.000000	1.000000	Only Theater
10	Bangur Nagar	Goregaon,Western Suburbs	19.167362	72.832252	1	Bangur Nagar	2	0	1.000000	0.000000	Only Shopping Mall
11	Juhu	Western Suburbs	19.1	72.83	0	Juhu	0	1	0.000000	1.000000	Only Theater
12	Charkop	Kandivali West,Western Suburbs	19.216182	72.830575	0	Charkop	0	1	0.000000	1.000000	Only Theater
13	Poisar	Kandivali West,Western Suburbs	19.204511	72.837639	1	Poisar	2	0	1.000000	0.000000	Only Shopping Mall
14	Mahavir Nagar	Kandivali West,Western Suburbs	19.211319	72.842737	1	Mahavir Nagar	1	0	1.000000	0.000000	Only Shopping Mall
15	Thakur village	Kandivali East,Western Suburbs	19.210206	72.87298	4	Thakur village	3	1	0.750000	0.250000	75% Shopping Mall/25% Theater
16	Dindoshi	Malad,Western Suburbs	19.176382	72.864891	1	Dindoshi	1	0	1.000000	0.000000	Only Shopping Mall
17	Sunder Nagar	Malad,Western Suburbs	19.175	72.842	1	Sunder Nagar	3	0	1.000000	0.000000	Only Shopping Mall
18	Irla	Vile Parle,Western Suburbs	19.108056	72.838056	0	Irla	0	1	0.000000	1.000000	Only Theater
19	Vile Parle	Western Suburbs	19.1	72.83	0	Vile Parle	0	1	0.000000	1.000000	Only Theater
20	Bhandup	Eastern Suburbs	19.14	72.93	1	Bhandup	1	0	1.000000	0.000000	Only Shopping Mall
21	Amrut Nagar	Ghatkopar,Eastern Suburbs	19.102077	72.912835	1	Amrut Nagar	2	0	1.000000	0.000000	Only Shopping Mall
22	Asalfa	Ghatkopar,Eastern Suburbs	19.091	72.901	2	Asalfa	2	1	0.666667	0.333333	66% Shopping Mall/33% Theater
23	Pant Nagar	Ghatkopar,Eastern Suburbs	19.08	72.91	2	Pant Nagar	2	1	0.666667	0.333333	66% Shopping Mall/33% Theater
24	Kanjurmarg	Eastern Suburbs	19.13	72.94	1	Kanjurmarg	1	0	1.000000	0.000000	Only Shopping Mall
25	Nahur	Mulund,Eastern Suburbs	19.157	72.941	2	Nahur	2	1	0.666667	0.333333	66% Shopping Mall/33% Theater
26	Chandivali	Powai,Eastern Suburbs	19.11	72.9	1	Chandivali	1	0	1.000000	0.000000	Only Shopping Mall
27	Hiranandani Gardens	Powai,Eastern Suburbs	19.118986	72.911767	1	Hiranandani Gardens	1	0	1.000000	0.000000	Only Shopping Mall
28	Indian Institute of Technology Bombay campus	Powai,Eastern Suburbs	19.133636	72.915358	1	Indian Institute of Technology Bombay campus	2	0	1.000000	0.000000	Only Shopping Mall
29	Vidyavihar	Eastern Suburbs	19.08	72.896	3	Vidyavihar	1	2	0.333333	0.666667	33% Shopping Mall/66% Theater
30	Vikhroli	Eastern Suburbs	19.11	72.94	1	Vikhroli	1	0	1.000000	0.000000	Only Shopping Mall
31	Chembur	Harbour Suburbs	19.051	72.894	0	Chembur	0	1	0.000000	1.000000	Only Theater
32	Deonar	Govandi,Harbour Suburbs	19.05	72.89	0	Deonar	0	1	0.000000	1.000000	Only Theater
33	Agripada	South Mumbai	18.9777	72.8273	2	Agripada	2	1	0.666667	0.333333	66% Shopping Mall/33% Theater
34	Altamount Road	South Mumbai	18.9681	72.8095	0	Altamount Road	0	1	0.000000	1.000000	Only Theater
35	Breach Candy	South Mumbai	18.967	72.805	0	Breach Candy	0	1	0.000000	1.000000	Only Theater
36	Carmichael Road	South Mumbai	18.9722	72.8113	0	Carmichael Road	0	2	0.000000	1.000000	Only Theater
37	Churchgate	South Mumbai	18.93	72.82	0	Churchgate	0	4	0.000000	1.000000	Only Theater
38	Cotton Green	South Mumbai	18.986209	72.844076	1	Cotton Green	2	0	1.000000	0.000000	Only Shopping Mall
39	Cuffe Parade	South Mumbai	18.91	72.81	3	Cuffe Parade	1	4	0.200000	0.800000	33% Shopping Mall/66% Theater
40	Cumbala Hill	South Mumbai	18.965833	72.805833	0	Cumbala Hill	0	1	0.000000	1.000000	Only Theater
41	Currey Road	South Mumbai	18.994	72.833	2	Currey Road	2	1	0.666667	0.333333	66% Shopping Mall/33% Theater
42	Dhobitalao	South Mumbai	18.9433	72.8286	0	Dhobitalao	0	1	0.000000	1.000000	Only Theater
43	Kala Ghoda	South Mumbai	18.9307	72.8331	0	Kala Ghoda	0	3	0.000000	1.000000	Only Theater
44	Kemps Corner	South Mumbai	18.9629	72.8054	0	Kemps Corner	0	1	0.000000	1.000000	Only Theater
45	Lower Parel	South Mumbai	18.995278	72.83	2	Lower Parel	2	1	0.666667	0.333333	66% Shopping Mall/33% Theater
46	Mahalaxmi	South Mumbai	18.983333	72.8	0	Mahalaxmi	0	1	0.000000	1.000000	Only Theater
47	Mahim	South Mumbai	19.035	72.84	0	Mahim	0	3	0.000000	1.000000	Only Theater
48	Marine Drive	South Mumbai	18.944	72.823	0	Marine Drive	0	1	0.000000	1.000000	Only Theater
49	Marine Lines	South Mumbai	18.9447	72.8244	0	Marine Lines	0	1	0.000000	1.000000	Only Theater
50	Mumbai Central	South Mumbai	18.9697	72.8194	0	Mumbai Central	0	1	0.000000	1.000000	Only Theater
51	Nariman Point	South Mumbai	18.926	72.823	0	Nariman Point	0	4	0.000000	1.000000	Only Theater
52	Prabhadevi	South Mumbai	19.0166	72.8295	3	Prabhadevi	1	3	0.250000	0.750000	33% Shopping Mall/66% Theater
53	Sion	South Mumbai	19.04	72.86	0	Sion	0	2	0.000000	1.000000	Only Theater
54	Worli	South Mumbai	19	72.815	2	Worli	2	1	0.666667	0.333333	66% Shopping Mall/33% Theater
55	C.G.S. colony	Antop Hill,South Mumbai	19.016378	72.856629	3	C.G.S. colony	1	4	0.200000	0.800000	33% Shopping Mall/66% Theater
56	Dagdi Chawl	Byculla,South Mumbai	18.977129	72.829131	2	Dagdi Chawl	2	1	0.666667	0.333333	66% Shopping Mall/33% Theater
57	Navy Nagar	Colaba,South Mumbai	18.9012	72.8101	1	Navy Nagar	1	0	1.000000	0.000000	Only Shopping Mall
58	Chor Bazaar	Kamathipura,South Mumbai	18.960321	72.827176	0	Chor Bazaar	0	1	0.000000	1.000000	Only Theater
59	Matunga	South Mumbai	19.01798	72.844763	0	Matunga	0	3	0.000000	1.000000	Only Theater
60	Parel	South Mumbai	18.99	72.84	2	Parel	2	1	0.666667	0.333333	66% Shopping Mall/33% Theater
61	Gowalia Tank	Tardeo,South Mumbai	18.96245	72.809703	0	Gowalia Tank	0	1	0.000000	1.000000	Only Theater
62	Dharavi	Mumbai	19.040208	72.85085	0	Dharavi	0	3	0.000000	1.000000	Only Theater
63	Thane	Mumbai	19.2	72.97	4	Thane	3	1	0.750000	0.250000	75% Shopping Mall/25% Theater

#Finally, let's visualize the resulting clusters

# create map
map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)

# set color scheme for the clusters
x = np.arange(kclusters)
ys = [i + x + (i*x)**2 for i in range(kclusters)]
colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))
rainbow = [colors.rgb2hex(i) for i in colors_array]

# add markers to the map
markers_colors = []
for lat, lon, poi, cluster,Labels, X1,X2 in zip(Mumbai_merged['Latitude'], Mumbai_merged['Longitude'], Mumbai_merged['Neighborhood'], Mumbai_merged['Cluster Labels'],Mumbai_merged['Labels'], Mumbai_merged['Shopping Mall_x'], Mumbai_merged['Theater_x']):
    label = folium.Popup(str(poi) + ' Area' +' has-'+ str(Labels)+'; Shopping Mall-'+str(X1)+' Theater-'+str(X2), parse_html=True)
    folium.CircleMarker(
        [lat, lon],
        radius=5,
        popup=label,
        color=rainbow[cluster-1],
        fill=True,
        fill_color=rainbow[cluster-1],
        fill_opacity=0.7).add_to(map_clusters)
       
map_clusters

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# get mumbai geojson data
mumbai_geo = r'https://raw.githubusercontent.com/datameet/PincodeBoundary/master/Mumbai/boundary.geojson' # geojson file

# get Mumbai area wise density information
Mumbai_den = pd.read_csv(r'C:\Users\Puneet\Desktop\Data_Casptone_project\Mumbai_density.csv')

Mumbai_den.head()

	Pincode	Area	Density_Square_Kilometer
0	400001	MUMBAI GPO	56253.20
1	400002	kalbadevi	56253.20
2	400003	Mandvi	56253.20
3	400004	girgaon	112734.44
4	400005	COLABA POST OFFICE	16867.76

# generate choropleth map to visualiaze the populatin density
map_mumbai = folium.Map(location=[latitude, longitude], zoom_start=11)

map_mumbai.choropleth(
    geo_data=mumbai_geo,
    data=Mumbai_den,
    columns=['Area', 'Density_Square_Kilometer'],
    key_on='feature.properties.name',
    fill_color='YlOrRd', 
    fill_opacity=0.7, 
    line_opacity=0.2,
    legend_name='Mumbai Population Density per KM'
)

# display map
map_mumbai

C:\ProgramData\Anaconda3\lib\site-packages\folium\folium.py:413: FutureWarning: The choropleth  method has been deprecated. Instead use the new Choropleth class, which has the same arguments. See the example notebook 'GeoJSON_and_choropleth' for how to do this.
  FutureWarning

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#Finally, let's visualize the resulting clusters and Mumbai city population density in a single map

# create map
map_clusters = map_mumbai

# set color scheme for the clusters
x = np.arange(kclusters)
ys = [i + x + (i*x)**2 for i in range(kclusters)]
colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))
rainbow = [colors.rgb2hex(i) for i in colors_array]

# add markers to the map
markers_colors = []
for lat, lon, poi, cluster,Labels, X1,X2 in zip(Mumbai_merged['Latitude'], Mumbai_merged['Longitude'], Mumbai_merged['Neighborhood'], Mumbai_merged['Cluster Labels'],Mumbai_merged['Labels'], Mumbai_merged['Shopping Mall_x'], Mumbai_merged['Theater_x']):
    label = folium.Popup(str(poi) + ' Area' +' has-'+ str(Labels)+'; Shopping Mall-'+str(X1)+' Theater-'+str(X2), parse_html=True)
    folium.CircleMarker(
        [lat, lon],
        radius=5,
        popup=label,
        color=rainbow[cluster-1],
        fill=True,
        fill_color=rainbow[cluster-1],
        fill_opacity=0.7).add_to(map_clusters)
       
map_clusters

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#The End